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Ensemble-based bag of features for automated classification of normal and COVID-19 CXR images
The medical and scientific communities are currently trying to treat infected patients and develop vaccines for preventing a future outbreak. In healthcare, machine learning is proven to be an efficient technology for helping to combat the COVID-19. Hospitals are now overwhelmed with the increased i...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8057743/ https://www.ncbi.nlm.nih.gov/pubmed/33897803 http://dx.doi.org/10.1016/j.bspc.2021.102656 |
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author | Ashour, Amira S. Eissa, Merihan M. Wahba, Maram A. Elsawy, Radwa A. Elgnainy, Hamada Fathy Tolba, Mohamed Saeed Mohamed, Waleed S. |
author_facet | Ashour, Amira S. Eissa, Merihan M. Wahba, Maram A. Elsawy, Radwa A. Elgnainy, Hamada Fathy Tolba, Mohamed Saeed Mohamed, Waleed S. |
author_sort | Ashour, Amira S. |
collection | PubMed |
description | The medical and scientific communities are currently trying to treat infected patients and develop vaccines for preventing a future outbreak. In healthcare, machine learning is proven to be an efficient technology for helping to combat the COVID-19. Hospitals are now overwhelmed with the increased infections of COVID-19 cases and given patients’ confidentiality and rights. It becomes hard to assemble quality medical image datasets in a timely manner. For COVID-19 diagnosis, several traditional computer-aided detection systems based on classification techniques were proposed. The bag-of-features (BoF) model has shown a promising potential in this domain. Thus, this work developed an ensemble-based BoF classification system for the COVID-19 detection. In this model, we proposed ensemble at the classification step of the BoF. The proposed system was evaluated and compared to different classification systems for different number of visual words to evaluate their effect on the classification efficiency. The results proved the superiority of the proposed ensemble-based BoF for the classification of normal and COVID19 chest X-ray (CXR) images compared to other classifiers. |
format | Online Article Text |
id | pubmed-8057743 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80577432021-04-21 Ensemble-based bag of features for automated classification of normal and COVID-19 CXR images Ashour, Amira S. Eissa, Merihan M. Wahba, Maram A. Elsawy, Radwa A. Elgnainy, Hamada Fathy Tolba, Mohamed Saeed Mohamed, Waleed S. Biomed Signal Process Control Article The medical and scientific communities are currently trying to treat infected patients and develop vaccines for preventing a future outbreak. In healthcare, machine learning is proven to be an efficient technology for helping to combat the COVID-19. Hospitals are now overwhelmed with the increased infections of COVID-19 cases and given patients’ confidentiality and rights. It becomes hard to assemble quality medical image datasets in a timely manner. For COVID-19 diagnosis, several traditional computer-aided detection systems based on classification techniques were proposed. The bag-of-features (BoF) model has shown a promising potential in this domain. Thus, this work developed an ensemble-based BoF classification system for the COVID-19 detection. In this model, we proposed ensemble at the classification step of the BoF. The proposed system was evaluated and compared to different classification systems for different number of visual words to evaluate their effect on the classification efficiency. The results proved the superiority of the proposed ensemble-based BoF for the classification of normal and COVID19 chest X-ray (CXR) images compared to other classifiers. Elsevier Ltd. 2021-07 2021-04-20 /pmc/articles/PMC8057743/ /pubmed/33897803 http://dx.doi.org/10.1016/j.bspc.2021.102656 Text en © 2021 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Ashour, Amira S. Eissa, Merihan M. Wahba, Maram A. Elsawy, Radwa A. Elgnainy, Hamada Fathy Tolba, Mohamed Saeed Mohamed, Waleed S. Ensemble-based bag of features for automated classification of normal and COVID-19 CXR images |
title | Ensemble-based bag of features for automated classification of normal and COVID-19 CXR images |
title_full | Ensemble-based bag of features for automated classification of normal and COVID-19 CXR images |
title_fullStr | Ensemble-based bag of features for automated classification of normal and COVID-19 CXR images |
title_full_unstemmed | Ensemble-based bag of features for automated classification of normal and COVID-19 CXR images |
title_short | Ensemble-based bag of features for automated classification of normal and COVID-19 CXR images |
title_sort | ensemble-based bag of features for automated classification of normal and covid-19 cxr images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8057743/ https://www.ncbi.nlm.nih.gov/pubmed/33897803 http://dx.doi.org/10.1016/j.bspc.2021.102656 |
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